title: Statistical challenges in the quantification of gunshot residue evidence.
abstract: The discharging of a gun results in the formation of extremely small particles known as gunshot residues (GSR). These may be deposited on the skin and clothing of the shooter, on other persons present, and on nearby items or surfaces. Several factors and their complex interactions affect the number of detectable GSR particles, which can deeply influence the conclusions drawn from likelihood ratios or posterior probabilities for prosecution hypotheses of interest. We present Bayesian network models for casework examples and demonstrate that probabilistic quantification of GSR evidence can be very sensitive to the assumptions concerning the model structure, prior probabilities, and the likelihood components. This finding has considerable implications for the use of statistical quantification of GSR evidence in the legal process.
authors.affiliation: Department of Mathematics and Statistics, University of Helsinki, FI-00014, Helsinki, Finland.
authors.forename: Romain
authors.initials: R
authors.lastname: Gauriot
authors.affiliation: National Bureau of Investigation Forensic Laboratory, Jokiniemenkuja 4, FI-01370, Vantaa, Finland.
authors.forename: Lawrence
authors.initials: L
authors.lastname: Gunaratnam
authors.affiliation: National Bureau of Investigation Forensic Laboratory, Jokiniemenkuja 4, FI-01370, Vantaa, Finland.|Department of Mathematics, Åbo Akademi University, Fänriksgatan 3B, FI-20500, Turku, Finland.
authors.forename: Rossana
authors.initials: R
authors.lastname: Moroni
authors.affiliation: National Bureau of Investigation Forensic Laboratory, Jokiniemenkuja 4, FI-01370, Vantaa, Finland.
authors.forename: Tapani
authors.initials: T
authors.lastname: Reinikainen
authors.affiliation: Department of Mathematics and Statistics, University of Helsinki, FI-00014, Helsinki, Finland.
authors.forename: Jukka
authors.initials: J
authors.lastname: Corander
keywords: Bayesian networks; contamination; forensic science; gunshot residue; model uncertainty; statistical quantification of evidence